1st Place Solution for the HECKTOR challenge

The official implementation of the winning solution for the MICCAI 2020 HEad and neCK TumOR segmentation challenge (HECKTOR).

Main requirements

PyTorch 1.6.0 (cuda 10.2)
SimpleITK 1.2.4 (ITK 4.13)
nibabel 3.1.1
skimage 0.17.2

Dataset

Train and test images are available through the competition website. The concise description of the dataset is present in notebooks/make_dataset.ipynb.

Data preprocessing

The data preprocessing consists of:

Resampling the pair of PET & CT images for each patient to a common reference space.
Extracting the region of interest (bounding box) of the size of 144x144x144 voxels.
Saving the transformed images in NIfTI format.

To prepare the dataset in an interactive manner, one can use notebooks/make_dataset.ipynb, that gives an explanation about each step. Alternatively, the fully automated data preprocessing can be performed by running src/data/make_dataset.py. All required parameters must be provided as a single config file in the YAML data format:

cd hecktor/src/data/
python make_dataset.py -p hecktor/config/make_dataset.yaml

Use /config/make_dataset.yaml to specify all required parameters.

Training

For training the model from scratch, one can use notebooks/model_train.ipynb setting all parameters right in the notebook. Otherwise, with all parameters written in the config file, one needs to run hecktor/model/train.py from its current directory:

cd hecktor/model/
python train.py -p hecktor/config/model_train.yaml

All parameters are described in hecktor/config/model_train.yaml that should be used as a template to build your own config file.

Inference

For inference, run the script hecktor/model/predict.py with parameters defined in the config file hecktor/config/model_predict.yaml:

cd hecktor/model/
python predict.py -p hecktor/config/model_predict.yaml

Model weights

To reproduce results presented in the paper on different train / validation folds, one must download and save pretrained weights in the folder hecktor/model/weights/. Weights of a single model are stored in files named {split}_best_model_weights.pt. IDs of the patients in the train / validation folds for each data split are stored in train_val_{split}.pkl files located in the folder hecktor/src/data/splits/.

In order to download weights of all pretrained model (eight models in total) built on the different train / validation, use the following link.

Example

Paper

If you use this code in you research, please cite the following paper (arXiv):

Iantsen A., Visvikis D., Hatt M. (2021) Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images. In: Andrearczyk V., Oreiller V., Depeursinge A. (eds) Head and Neck Tumor Segmentation. HECKTOR 2020. Lecture Notes in Computer Science, vol 12603. Springer, Cham. https://doi.org/10.1007/978-3-030-67194-5_4

Xinya-liuliu / hecktor-1